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PCANet is a simple network using Principal Component Analysis (PCA) for image classification and obtained high accuracies on a variety of datasets. PCA projects explanatory variables on a subspace that the first component has the largest variance. On the other hand, Partial Least Squares (PLS) regression projects explanatory variables on a subspace that the first component has the largest covariance...
Handwritten digit recognition is a typical image classification problem. Convolutional neural networks, also known as ConvNets, are powerful classification models for such tasks. As different languages have different styles and shapes of their numeral digits, accuracy rates of the models vary from each other and from language to language. However, unsupervised pre-training in such situation has shown...
Advances in unsupervised learning have allowed the efficient learning of feature representations directly from large sets of unlabeled data instead of using traditional handcrafted features. However, improving algorithms to increase the quality of these representations in the absence of labeled data is still an area of active research. This paper evaluates visual features learned through unsupervised...
In this paper, General Purpose Graphical Processing Unit (GPGPU) based concurrent implementation of handwritten digit classifier is presented. Different styles of handwriting make it difficult to recognize a pattern but using neural network, it is not a difficult task to perform. Different softwares like torch and MATLAB provide the support of multiple training algorithms to train a network. By choosing...
In this work, an interactive visual system MICS is presented for large-scale brain CT image classification. Automatic feature extraction algorithms are added in MICS to improve system efficiency and classification accuracy. In visualization part, we designed an interactive feature extraction interface, enable users to extract and fine-tune image features according to specific requirements. In addition,...
The k-nearest-neighbour classifiers (k-NN) have been one of the simplest yet most effective approaches to instance based learning problem for image classification. However, with the growth of the size of image datasets and the number of dimensions of image descriptors, popularity of k-NNs has decreased due to their significant storage requirements and computational costs. In this paper we propose...
This paper presents the impact of automatic feature extraction used in a deep learning architecture such as Convolutional Neural Network (CNN). Recently CNN has become a very popular tool for image classification which can automatically extract features, learn and classify them. It is a common belief that CNN can always perform better than other well-known classifiers. However, there is no systematic...
At present, collaborative representation based classification (CRC) is widely used in many pattern classification and recognition tasks. Meanwhile, spatial pyramid matching (SPM) method, which considers the spatial information in representing the image, is efficient for image classification. However, for SPM, the weights to evaluate the representation of different subregions are fixed. In this paper,...
Artificial neural networks (ANN) with deep learning using convolutional neural networks have recently achieved good results in various challenging problems. The HMAX is yet another deep architecture that could offer similar performance but with less training cycles required. In this paper, we extended the performance of HMAX by aggregating several HMAX networks together to achieve state-of-the-art...
The goal of the Deep learning methods is learning feature hierarchies with features from higher levels to lower level features of the hierarchy. The major contribution of this paper is to show how to extract features and train an image classification system on large-scale datasets. This method is an improvement of our recent work. The training is carried out by the combination of the most used methods...
Sparse Coding is a widely used method to represent an image. However, sparse coding and its improved algorithms have the problem of complex computation and long running time and so on. For these problems, we propose an image classification method based on hash codes and space pyramid, which encodes local feature points with hash codes instead of sparse coding. Firstly, extract the local feature points...
Hyperspectral imagery is being widely used for accurate object detection and terrain feature classification. Modern imaging spectrometers produce huge amounts of data that are compressed onboard and downloaded to ground stations to be processed. Increasing spectral resolution and data acquisition rates demand more efficient compression techniques to meet downlink bandwidth restrictions. A different...
This paper presents the results of the ICFHR2016 Competition on the Classification of Medieval Handwritings in Latin Script (CLaMM), jointly organized by Computer Scientists and Humanists (paleographers). This work aims at providing a rich database of European medieval manuscripts to the community on Handwriting Analysis and Recognition. At this competition, we proposed two independent classification...
This paper proposes an aggregate (or mixture) of ensemble models for image retrieval based on deep Convolutional Neural Networks (CNN). It utilizes two kinds of deep learning networks, AlexNet and Network In Network (NIN), to obtain image features, and to compute weighted average feature vectors for image retrieval. Based on experimental results, the aggregate ensemble architecture effectively enhances...
Image emotion analysis is a new and challenging research direction that gains more and more attention in the research community. Most previous works in this field only use common or generic features, and have hard restrictions on training images, such as scale, resolution, etc. Inspired by scale-space theories and psychology theories of color, we propose a procedure to extract interpretive features...
In today's era multimedia is playing a vital role. The Number of videos are generated in huge amount which implies the need to build the classification system based on contents of videos. The paper proposes video classification approach with data mining classifiers applied on Linde Buzo Gray Vector Quantization Codebooks of the video frames. Total 68 variations of proposed classification method with...
We consider the use of transfer learning, via the use of deep Convolutional Neural Networks (CNN) for the image classification problem posed within the context of X-ray baggage security screening. The use of a deep multi-layer CNN approach, traditionally requires large amounts of training data, in order to facilitate construction of a complex complete end-to-end feature extraction, representation...
Deep Convolutional Neural Networks (CNN) have recently been shown to outperform previous state of the art approaches for image classification. Their success must in parts be attributed to the availability of large labeled training sets such as provided by the ImageNet benchmarking initiative. When training data is scarce, however, CNNs have proven to fail to learn descriptive features. Recent research...
Commonly, HoG/SVM classifier uses rectangular images for HoG feature descriptor extraction and training. This means significant additional work has to be done to process irrelevant pixels belonging to the background surrounding the object of interest. While some objects may indeed be square or rectangular, most of objects are not easily representable by simple geometric shapes. In Bitmap-HoG approach...
A label consistent recursive least squares dictionary learning algorithm, LC-RLSDLA, is proposed to learn discriminative dictionaries for image classification based on sparse coding. The class label information and a label consistency term are used in the cost function to enforce discriminability among the sparse codes. Two operation modes are derived for the LC-RLSDLA: the supervised learning mode,...
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